This paper describes a model driven methodology in order to implement an interoperable communication architecture supporting TSO-DSO information exchange. The model driven methodology goes through Smart Grid Architecture Model interoperability layers and leverage international standards. The Use Case approach is utilized for identification of information exchange requirements, which are materialized through Business Objects gap analysis against existing standardized IEC CIM (Common Information Model) profiles. Determined set of standardized Business Objects can be implemented using several communication technologies. Some of these up-to-date technologies are provided by off the shelf solutions such as ECCo SP, a secure and scalable platform provided by ENTSO-E.
There has been an increasing trend of integrating photovoltaic power plants (PVs). One of the important challenges for distribution system operators is to evaluate the total installed power of a PV that a particular network can host (or PV hosting capacity) while keeping voltage and element constraints within required limits. The major drawback of the existing methods for calculating PV hosting capacity is that they use the same installed power of the PV systems for all simulated PVs, as these methods do not use external data sources about building roofs. As a consequence, this has a significant impact on the final accuracy of the results. This paper presents a probabilistic methodology for calculating the PV hosting capacity in low voltage (LV) networks. The main contribution of this paper is the improved modeling of PV generation using actual building roof data when calculating the PV hosting capacity, as every building is treated according to its actual solar potential. Monte Carlo simulations with incorporated stochastic consumption and PV generation models are utilized for load flow calculations of the actual LV network. The simulation results presented in this paper prove that the proposed methodology increases the accuracy of the final PV hosting capacity calculations.Energies 2019, 12, 4086 2 of 15 capacity, various scenarios must be calculated for different PV locations and penetrations. This can provide a statistical description of the possible levels of PV penetration while voltages and element loadings are still within the required range.Many papers have dealt with this kind of problem. The obtained results are usually presented as a network violation probability, depending on the number of consumers with installed PVs or total nominal power of PVs in the network. The paper [7] described low voltage (LV) network planning based on the Monte Carlo approach, taking into account different random distributed generation (DG) locations. The probabilities of voltage levels and the maximum PV penetration limits were assessed. The study in Reference [8] proposed a similar approach, where the PV hosting capacity was evaluated considering the probability of voltage limit violation occurrences at the customer level, according to different PV power factors. The approach developed by the Electric Power Research Institute (EPRI) [9] also proposed a stochastic approach when creating PV deployment scenarios. The stochastic nature of the analysis takes into account the uncertainty in the size and location of the future PV systems [9]. In Reference [10], the work proposed a Monte Carlo-based technique to assess the impacts of different PV penetrations on LV networks, in order to estimate their corresponding hosting capabilities. Similarly, the authors in Reference [11] proposed a probabilistic methodology for evaluating the impacts of PVs, electric heat pumps, micro combined heat and power units, and electrical vehicles on network operation states. The authors in Reference [12] studied how to improve PV hosting capacity...
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